Using an Improved Regularization Method and Rigid Transformation for Super-Resolution Applied on MRI Data

Matina Christina Zerva; Giannis Chantas; Lisimachos Paul Kondi

doi:10.20944/preprints202410.1291.v1

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 16 October 2024 / Approved: 16 October 2024 / Online: 17 October 2024 (07:55:21 CEST)

Super-resolution (SR) techniques have shown great promise in enhancing the resolution of MRI images, which are often limited by hardware constraints and acquisition time. Regularization-based methods, which incorporate prior knowledge into the SR process, have been especially effective in improving image quality and mitigating the effects of noise and blur. In this paper, we propose an advanced regularization method for MRI super-resolution that balances high-frequency detail preservation with noise suppression. By leveraging spatially adaptive regularization techniques and a robust denoising process, the proposed method outperforms traditional SR algorithms, as demonstrated on real-world MRI datasets.

MRI; Super-resolution; Regularization method

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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Using an Improved Regularization Method and Rigid Transformation for Super-Resolution Applied on MRI Data

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